library(tidyverse) # for data cleaning and plotting
library(googlesheets4) # for reading googlesheet data
library(lubridate) # for date manipulation
library(openintro) # for the abbr2state() function
library(palmerpenguins)# for Palmer penguin data
library(maps) # for map data
library(ggmap) # for mapping points on maps
library(gplots) # for col2hex() function
library(RColorBrewer) # for color palettes
library(sf) # for working with spatial data
library(leaflet) # for highly customizable mapping
library(carData) # for Minneapolis police stops data
library(ggthemes) # for more themes (including theme_map())
gs4_deauth() # To not have to authorize each time you knit.
theme_set(theme_minimal())
# Starbucks locations
Starbucks <- read_csv("https://www.macalester.edu/~ajohns24/Data/Starbucks.csv")
starbucks_us_by_state <- Starbucks %>%
filter(Country == "US") %>%
count(`State/Province`) %>%
mutate(state_name = str_to_lower(abbr2state(`State/Province`)))
# Lisa's favorite St. Paul places - example for you to create your own data
favorite_stp_by_lisa <- tibble(
place = c("Home", "Macalester College", "Adams Spanish Immersion",
"Spirit Gymnastics", "Bama & Bapa", "Now Bikes",
"Dance Spectrum", "Pizza Luce", "Brunson's"),
long = c(-93.1405743, -93.1712321, -93.1451796,
-93.1650563, -93.1542883, -93.1696608,
-93.1393172, -93.1524256, -93.0753863),
lat = c(44.950576, 44.9378965, 44.9237914,
44.9654609, 44.9295072, 44.9436813,
44.9399922, 44.9468848, 44.9700727)
)
#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")
If you were not able to get set up on GitHub last week, go here and get set up first. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):
keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).Put your name at the top of the document.
For ALL graphs, you should include appropriate labels.
Feel free to change the default theme, which I currently have set to theme_minimal().
Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!
When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.
These exercises will reiterate what you learned in the “Mapping data with R” tutorial. If you haven’t gone through the tutorial yet, you should do that first.
ggmap)Starbucks locations to a world map. Add an aesthetic to the world map that sets the color of the points according to the ownership type. What, if anything, can you deduce from this visualization?world <- get_stamenmap(
bbox = c(left = -180, bottom = -57, right = 179, top = 82.1),
maptype = "terrain",
zoom = 2)
ggmap(world) +
geom_point(data = Starbucks,
aes(x = Longitude, y = Latitude, color = `Ownership Type` ),
alpha = .3,
size = .1) +
theme_map()+
labs(title = "Global Starbucks Locations by Ownership Type")
Company Owned and Licensed locations are the most common worldwide. There are next to no Joint Venture locations in North America, a few of them in eastern Europe, and exclusively Joint Venture in Japan.
twincities <- get_stamenmap(
bbox = c(left = -93.29, bottom = 44.87, right = -92.96, top = 45.03),
maptype = "terrain",
zoom = 11)
ggmap(twincities) +
geom_point(data = Starbucks,
aes(x = Longitude, y = Latitude))+
theme_map()+
labs(title = "Starbucks in Twin Cities Area")
A higher zoom make a much more detailed map. When zoom is say 2, like in the first plot, my map just looked like a big brown blob. By increasing the zoom to 11 I was able to see streets and names and rivers.
get_stamenmap() in help and look at maptype). Include a map with one of the other map types.twincities <- get_stamenmap(
bbox = c(left = -93.29, bottom = 44.87, right = -92.96, top = 45.03),
maptype = "watercolor",
zoom = 11)
ggmap(twincities) +
geom_point(data = Starbucks,
aes(x = Longitude, y = Latitude))+
theme_map()
annotate() function (see ggplot2 cheatsheet).twincities <- get_stamenmap(
bbox = c(left = -93.29, bottom = 44.87, right = -92.96, top = 45.03),
maptype = "terrain",
zoom = 11)
ggmap(twincities) +
geom_point(data = Starbucks,
aes(x = Longitude, y = Latitude))+
annotate(geom = "text", x = -93.16, y = 44.94, label = "Mac", size = 3)
theme_map()
## List of 93
## $ line :List of 6
## ..$ colour : chr "black"
## ..$ size : num 0.409
## ..$ linetype : num 1
## ..$ lineend : chr "butt"
## ..$ arrow : logi FALSE
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_line" "element"
## $ rect :List of 5
## ..$ fill : chr "white"
## ..$ colour : chr "black"
## ..$ size : num 0.409
## ..$ linetype : num 1
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_rect" "element"
## $ text :List of 11
## ..$ family : chr ""
## ..$ face : chr "plain"
## ..$ colour : chr "black"
## ..$ size : num 9
## ..$ hjust : num 0.5
## ..$ vjust : num 0.5
## ..$ angle : num 0
## ..$ lineheight : num 0.9
## ..$ margin : 'margin' num [1:4] 0points 0points 0points 0points
## .. ..- attr(*, "unit")= int 8
## ..$ debug : logi FALSE
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ title : NULL
## $ aspect.ratio : NULL
## $ axis.title : list()
## ..- attr(*, "class")= chr [1:2] "element_blank" "element"
## $ axis.title.x :List of 11
## ..$ family : NULL
## ..$ face : NULL
## ..$ colour : NULL
## ..$ size : NULL
## ..$ hjust : NULL
## ..$ vjust : num 1
## ..$ angle : NULL
## ..$ lineheight : NULL
## ..$ margin : 'margin' num [1:4] 2.25points 0points 0points 0points
## .. ..- attr(*, "unit")= int 8
## ..$ debug : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ axis.title.x.top :List of 11
## ..$ family : NULL
## ..$ face : NULL
## ..$ colour : NULL
## ..$ size : NULL
## ..$ hjust : NULL
## ..$ vjust : num 0
## ..$ angle : NULL
## ..$ lineheight : NULL
## ..$ margin : 'margin' num [1:4] 0points 0points 2.25points 0points
## .. ..- attr(*, "unit")= int 8
## ..$ debug : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ axis.title.x.bottom : NULL
## $ axis.title.y :List of 11
## ..$ family : NULL
## ..$ face : NULL
## ..$ colour : NULL
## ..$ size : NULL
## ..$ hjust : NULL
## ..$ vjust : num 1
## ..$ angle : num 90
## ..$ lineheight : NULL
## ..$ margin : 'margin' num [1:4] 0points 2.25points 0points 0points
## .. ..- attr(*, "unit")= int 8
## ..$ debug : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ axis.title.y.left : NULL
## $ axis.title.y.right :List of 11
## ..$ family : NULL
## ..$ face : NULL
## ..$ colour : NULL
## ..$ size : NULL
## ..$ hjust : NULL
## ..$ vjust : num 0
## ..$ angle : num -90
## ..$ lineheight : NULL
## ..$ margin : 'margin' num [1:4] 0points 0points 0points 2.25points
## .. ..- attr(*, "unit")= int 8
## ..$ debug : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ axis.text : list()
## ..- attr(*, "class")= chr [1:2] "element_blank" "element"
## $ axis.text.x :List of 11
## ..$ family : NULL
## ..$ face : NULL
## ..$ colour : NULL
## ..$ size : NULL
## ..$ hjust : NULL
## ..$ vjust : num 1
## ..$ angle : NULL
## ..$ lineheight : NULL
## ..$ margin : 'margin' num [1:4] 1.8points 0points 0points 0points
## .. ..- attr(*, "unit")= int 8
## ..$ debug : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ axis.text.x.top :List of 11
## ..$ family : NULL
## ..$ face : NULL
## ..$ colour : NULL
## ..$ size : NULL
## ..$ hjust : NULL
## ..$ vjust : num 0
## ..$ angle : NULL
## ..$ lineheight : NULL
## ..$ margin : 'margin' num [1:4] 0points 0points 1.8points 0points
## .. ..- attr(*, "unit")= int 8
## ..$ debug : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ axis.text.x.bottom : NULL
## $ axis.text.y :List of 11
## ..$ family : NULL
## ..$ face : NULL
## ..$ colour : NULL
## ..$ size : NULL
## ..$ hjust : num 1
## ..$ vjust : NULL
## ..$ angle : NULL
## ..$ lineheight : NULL
## ..$ margin : 'margin' num [1:4] 0points 1.8points 0points 0points
## .. ..- attr(*, "unit")= int 8
## ..$ debug : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ axis.text.y.left : NULL
## $ axis.text.y.right :List of 11
## ..$ family : NULL
## ..$ face : NULL
## ..$ colour : NULL
## ..$ size : NULL
## ..$ hjust : num 0
## ..$ vjust : NULL
## ..$ angle : NULL
## ..$ lineheight : NULL
## ..$ margin : 'margin' num [1:4] 0points 0points 0points 1.8points
## .. ..- attr(*, "unit")= int 8
## ..$ debug : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ axis.ticks : list()
## ..- attr(*, "class")= chr [1:2] "element_blank" "element"
## $ axis.ticks.x : NULL
## $ axis.ticks.x.top : NULL
## $ axis.ticks.x.bottom : NULL
## $ axis.ticks.y : NULL
## $ axis.ticks.y.left : NULL
## $ axis.ticks.y.right : NULL
## $ axis.ticks.length : 'simpleUnit' num 2.25points
## ..- attr(*, "unit")= int 8
## $ axis.ticks.length.x : NULL
## $ axis.ticks.length.x.top : NULL
## $ axis.ticks.length.x.bottom: NULL
## $ axis.ticks.length.y : NULL
## $ axis.ticks.length.y.left : NULL
## $ axis.ticks.length.y.right : NULL
## $ axis.line : list()
## ..- attr(*, "class")= chr [1:2] "element_blank" "element"
## $ axis.line.x : NULL
## $ axis.line.x.top : NULL
## $ axis.line.x.bottom : NULL
## $ axis.line.y : NULL
## $ axis.line.y.left : NULL
## $ axis.line.y.right : NULL
## $ legend.background :List of 5
## ..$ fill : NULL
## ..$ colour : logi NA
## ..$ size : NULL
## ..$ linetype : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_rect" "element"
## $ legend.margin : 'margin' num [1:4] 4.5points 4.5points 4.5points 4.5points
## ..- attr(*, "unit")= int 8
## $ legend.spacing : 'simpleUnit' num 9points
## ..- attr(*, "unit")= int 8
## $ legend.spacing.x : NULL
## $ legend.spacing.y : NULL
## $ legend.key :List of 5
## ..$ fill : chr "white"
## ..$ colour : logi NA
## ..$ size : NULL
## ..$ linetype : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_rect" "element"
## $ legend.key.size : 'simpleUnit' num 1.2lines
## ..- attr(*, "unit")= int 3
## $ legend.key.height : NULL
## $ legend.key.width : NULL
## $ legend.text :List of 11
## ..$ family : NULL
## ..$ face : NULL
## ..$ colour : NULL
## ..$ size : 'rel' num 0.8
## ..$ hjust : NULL
## ..$ vjust : NULL
## ..$ angle : NULL
## ..$ lineheight : NULL
## ..$ margin : NULL
## ..$ debug : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ legend.text.align : NULL
## $ legend.title :List of 11
## ..$ family : NULL
## ..$ face : NULL
## ..$ colour : NULL
## ..$ size : NULL
## ..$ hjust : num 0
## ..$ vjust : NULL
## ..$ angle : NULL
## ..$ lineheight : NULL
## ..$ margin : NULL
## ..$ debug : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ legend.title.align : NULL
## $ legend.position : num [1:2] 0 0
## $ legend.direction : NULL
## $ legend.justification : num [1:2] 0 0
## $ legend.box : NULL
## $ legend.box.just : NULL
## $ legend.box.margin : 'margin' num [1:4] 0cm 0cm 0cm 0cm
## ..- attr(*, "unit")= int 1
## $ legend.box.background : list()
## ..- attr(*, "class")= chr [1:2] "element_blank" "element"
## $ legend.box.spacing : 'simpleUnit' num 9points
## ..- attr(*, "unit")= int 8
## $ panel.background : list()
## ..- attr(*, "class")= chr [1:2] "element_blank" "element"
## $ panel.border : list()
## ..- attr(*, "class")= chr [1:2] "element_blank" "element"
## $ panel.spacing : 'simpleUnit' num 0lines
## ..- attr(*, "unit")= int 3
## $ panel.spacing.x : NULL
## $ panel.spacing.y : NULL
## $ panel.grid : list()
## ..- attr(*, "class")= chr [1:2] "element_blank" "element"
## $ panel.grid.major : NULL
## $ panel.grid.minor :List of 6
## ..$ colour : NULL
## ..$ size : 'rel' num 0.5
## ..$ linetype : NULL
## ..$ lineend : NULL
## ..$ arrow : logi FALSE
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_line" "element"
## $ panel.grid.major.x : NULL
## $ panel.grid.major.y : NULL
## $ panel.grid.minor.x : NULL
## $ panel.grid.minor.y : NULL
## $ panel.ontop : logi FALSE
## $ plot.background : list()
## ..- attr(*, "class")= chr [1:2] "element_blank" "element"
## $ plot.title :List of 11
## ..$ family : NULL
## ..$ face : NULL
## ..$ colour : NULL
## ..$ size : 'rel' num 1.2
## ..$ hjust : num 0
## ..$ vjust : num 1
## ..$ angle : NULL
## ..$ lineheight : NULL
## ..$ margin : 'margin' num [1:4] 0points 0points 4.5points 0points
## .. ..- attr(*, "unit")= int 8
## ..$ debug : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ plot.title.position : chr "panel"
## $ plot.subtitle :List of 11
## ..$ family : NULL
## ..$ face : NULL
## ..$ colour : NULL
## ..$ size : NULL
## ..$ hjust : num 0
## ..$ vjust : num 1
## ..$ angle : NULL
## ..$ lineheight : NULL
## ..$ margin : 'margin' num [1:4] 0points 0points 4.5points 0points
## .. ..- attr(*, "unit")= int 8
## ..$ debug : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ plot.caption :List of 11
## ..$ family : NULL
## ..$ face : NULL
## ..$ colour : NULL
## ..$ size : 'rel' num 0.8
## ..$ hjust : num 1
## ..$ vjust : num 1
## ..$ angle : NULL
## ..$ lineheight : NULL
## ..$ margin : 'margin' num [1:4] 4.5points 0points 0points 0points
## .. ..- attr(*, "unit")= int 8
## ..$ debug : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ plot.caption.position : chr "panel"
## $ plot.tag :List of 11
## ..$ family : NULL
## ..$ face : NULL
## ..$ colour : NULL
## ..$ size : 'rel' num 1.2
## ..$ hjust : num 0.5
## ..$ vjust : num 0.5
## ..$ angle : NULL
## ..$ lineheight : NULL
## ..$ margin : NULL
## ..$ debug : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ plot.tag.position : chr "topleft"
## $ plot.margin : 'margin' num [1:4] 4.5points 4.5points 4.5points 4.5points
## ..- attr(*, "unit")= int 8
## $ strip.background :List of 5
## ..$ fill : chr "grey85"
## ..$ colour : chr "grey20"
## ..$ size : NULL
## ..$ linetype : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_rect" "element"
## $ strip.background.x : NULL
## $ strip.background.y : NULL
## $ strip.placement : chr "inside"
## $ strip.text :List of 11
## ..$ family : NULL
## ..$ face : NULL
## ..$ colour : chr "grey10"
## ..$ size : 'rel' num 0.8
## ..$ hjust : NULL
## ..$ vjust : NULL
## ..$ angle : NULL
## ..$ lineheight : NULL
## ..$ margin : 'margin' num [1:4] 3.6points 3.6points 3.6points 3.6points
## .. ..- attr(*, "unit")= int 8
## ..$ debug : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ strip.text.x : NULL
## $ strip.text.y :List of 11
## ..$ family : NULL
## ..$ face : NULL
## ..$ colour : NULL
## ..$ size : NULL
## ..$ hjust : NULL
## ..$ vjust : NULL
## ..$ angle : num -90
## ..$ lineheight : NULL
## ..$ margin : NULL
## ..$ debug : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ strip.switch.pad.grid : 'simpleUnit' num 2.25points
## ..- attr(*, "unit")= int 8
## $ strip.switch.pad.wrap : 'simpleUnit' num 2.25points
## ..- attr(*, "unit")= int 8
## $ strip.text.y.left :List of 11
## ..$ family : NULL
## ..$ face : NULL
## ..$ colour : NULL
## ..$ size : NULL
## ..$ hjust : NULL
## ..$ vjust : NULL
## ..$ angle : num 90
## ..$ lineheight : NULL
## ..$ margin : NULL
## ..$ debug : NULL
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi TRUE
## - attr(*, "validate")= logi TRUE
geom_map())The example I showed in the tutorial did not account for population of each state in the map. In the code below, a new variable is created, starbucks_per_10000, that gives the number of Starbucks per 10,000 people. It is in the starbucks_with_2018_pop_est dataset.
census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>%
separate(state, into = c("dot","state"), extra = "merge") %>%
select(-dot) %>%
mutate(state = str_to_lower(state))
starbucks_with_2018_pop_est <-
starbucks_us_by_state %>%
left_join(census_pop_est_2018,
by = c("state_name" = "state")) %>%
mutate(starbucks_per_10000 = (n/est_pop_2018)*10000)
dplyr review: Look through the code above and describe what each line of code does.
Create a choropleth map that shows the number of Starbucks per 10,000 people on a map of the US. Use a new fill color, add points for all Starbucks in the US (except Hawaii and Alaska), add an informative title for the plot, and include a caption that says who created the plot (you!). Make a conclusion about what you observe.
starbucks_us_by_state <- Starbucks %>%
filter(Country == "US") %>%
count(`State/Province`) %>%
mutate(state_name = str_to_lower(abbr2state(`State/Province`)))
states_map <- map_data("state")
starbucks_with_2018_pop_est %>%
ggplot() +
geom_map(map = states_map,
aes(map_id = state_name,
fill = starbucks_per_10000)) +
expand_limits(x = states_map$long, y = states_map$lat) +
geom_point(data = Starbucks %>% filter(Country == "US",
`State/Province` != "AK",
`State/Province` != "HI"),
aes(x = Longitude, y = Latitude),
size = .05,
alpha = .2,
color = "goldenrod") +
theme_map()+
labs(title = "Number of Starbucks locations per 10,000 people by state", subtitle = "map by Kelsey Stender")
leaflet)tibble() function that has 10-15 rows of your favorite places. The columns will be the name of the location, the latitude, the longitude, and a column that indicates if it is in your top 3 favorite locations or not. For an example of how to use tibble(), look at the favorite_stp_by_lisa I created in the data R code chunk at the beginning.favorite_places<-tibble(
place = c("Calderon's", "Stender's", "home", "New_house", "Raku", "Rocklands", "apartment", "Eme's", "Macalester", "Magic_noodle"),
long = c(-97.85, -122.32, -77.13, -76.87, -77.097, -77.34, -93.16, -93.25, -93.16, -93.15),
lat = c(30.54, 47.68, 38.97, 38.94, 38.98, 39.09, 44.93, 44.98, 44.94, 44.95),
top_three = c("second", "third", "first", "not", "not", "not", "not", "not", "not", "not")
)
leaflet map that uses circles to indicate your favorite places. Label them with the name of the place. Choose the base map you like best. Color your 3 favorite places differently than the ones that are not in your top 3 (HINT: colorFactor()). Add a legend that explains what the colors mean.favorite_colors<-colorFactor(palette = c("blue", "goldenrod", "green", "pink"),
levels = c("first", "second", "third", "not"))
leaflet(data = favorite_places) %>%
addProviderTiles(providers$CartoDB.DarkMatter) %>%
addCircles(lng = ~long,
lat = ~lat,
label = ~place,
weight = 10,
opacity = 1,
color = ~favorite_colors(top_three))
leaflet(data = favorite_places) %>%
addProviderTiles(providers$CartoDB.DarkMatter) %>%
addCircles(lng = ~long,
lat = ~lat,
label = ~place,
weight = 10,
opacity = 1,
color = col2hex("darkred")) %>%
addPolylines(lng = ~long,
lat = ~lat,
color = col2hex("darkred"))
This section will revisit some datasets we have used previously and bring in a mapping component.
The data come from Washington, DC and cover the last quarter of 2014.
Two data tables are available:
Trips contains records of individual rentalsStations gives the locations of the bike rental stationsHere is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}. This code reads in the large dataset right away.
data_site <-
"https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds"
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")
Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. This time, plot the points on top of a map. Use any of the mapping tools you’d like.dcarea <- get_stamenmap(
bbox = c(left = -77.1, bottom = 38.85, right = -76.94, top = 38.94),
maptype = "terrain",
zoom = 13)
station_departures<-
Stations%>%
mutate(sstation = name)%>%
left_join(Trips,
by = "sstation")%>%
group_by(name, lat, long)%>%
count()
ggmap(dcarea)+
geom_point(data = station_departures,
aes(x = long, y = lat, color = n),
size=1,
alpha = 1) +
theme_map()
station_departures_casual<-
Stations%>%
mutate(sstation = name)%>%
left_join(Trips,
by = "sstation")%>%
group_by(name, lat, long)%>%
summarize(casual_percent = mean(client == "Casual"))
ggmap(dcarea)+
geom_point(data = station_departures_casual,
aes(x = long, y = lat, color = casual_percent),
size=1,
alpha = 1) +
theme_map()
The departures in the more tourist-y areas of DC have a much higher percentage of casual departures than other areas of DC.
The following exercises will use the COVID-19 data from the NYT.
covid19%>%
filter(date %in% as.Date("2020-09-30"))%>%
filter(!state %in% c("alaska","hawaii","guam","virgin islands", "puerto rico", "northern mariana islands"))%>%
mutate(state = str_to_lower(state))%>%
left_join(census_pop_est_2018)%>%
ggplot()+
geom_map(map = states_map,
aes(map_id = state,
fill = cases))+
expand_limits(x = states_map$long, y = states_map$lat)+
theme_map()+
labs(title = "Most Recent Cumulative COVID-19 Cases")
This map does not take population into account. California has 39 million people, South Dakota has 884,000 people. If 10% of California’s population got COVID, 3.9 million people would be infected- if 100% of the people in South Dakota got COVID, there would still be more than 4 times as many people in California with COVID.
covid19%>%
filter(date %in% as.Date("2020-09-30"))%>%
filter(!state %in% c("alaska","hawaii","guam","virgin islands", "puerto rico", "northern mariana islands"))%>%
mutate(state = str_to_lower(state))%>%
left_join(census_pop_est_2018)%>%
mutate(covid_10000 = (cases/est_pop_2018)*10000)%>%
ggplot()+
geom_map(map = states_map,
aes(map_id = state,
fill = covid_10000))+
expand_limits(x = states_map$long, y = states_map$lat)+
theme_map()+
labs(title = "Most Recent Cumulative COVID-19 Cases per 10,000 People")
13. CHALLENGE Choose 4 dates spread over the time period of the data and create the same map as in exercise 12 for each of the dates. Display the four graphs together using faceting. What do you notice?
covid19%>%
filter(date %in% c(as.Date("2020-03-14"),
as.Date("2020-05-25"),
as.Date("2020-06-23"),
as.Date("2020-09-27")))%>%
filter(!state %in% c("alaska","hawaii","guam","virgin islands", "puerto rico", "northern mariana islands"))%>%
mutate(state = str_to_lower(state))%>%
left_join(census_pop_est_2018)%>%
mutate(covid_10000 = (cases/est_pop_2018)*10000)%>%
ggplot()+
geom_map(map = states_map,
aes(map_id = state,
fill = covid_10000))+
expand_limits(x = states_map$long, y = states_map$lat)+
theme_map()+
theme(legend.position = "right",
legend.background = element_blank())+
facet_wrap(vars(date))+
labs(title = "Cumulative COVID-19 Cases per 10,000 People Over Time")
These exercises use the datasets MplsStops and MplsDemo from the carData library. Search for them in Help to find out more information.
MplsStops dataset to find out how many stops there were for each neighborhood and the proportion of stops that were for a suspicious vehicle or person. Sort the results from most to least number of stops. Save this as a dataset called mpls_suspicious and display the table.mpls_suspicious<-
MplsStops%>%
group_by(neighborhood, problem)%>%
summarise(number = n())%>%
mutate(prop_sus = number/sum(number))%>%
filter(problem == "suspicious")%>%
arrange(desc(prop_sus))
Use a leaflet map and the MplsStops dataset to display each of the stops on a map as a small point. Color the points differently depending on whether they were for suspicious vehicle/person or a traffic stop (the problem variable). HINTS: use addCircleMarkers, set stroke = FAlSE, use colorFactor() to create a palette.
Save the folder from moodle called Minneapolis_Neighborhoods into your project/repository folder for this assignment. Make sure the folder is called Minneapolis_Neighborhoods. Use the code below to read in the data and make sure to delete the eval=FALSE. Although it looks like it only links to the .sph file, you need the entire folder of files to create the mpls_nbhd data set. These data contain information about the geometries of the Minneapolis neighborhoods. Using the mpls_nbhd dataset as the base file, join the mpls_suspicious and MplsDemo datasets to it by neighborhood (careful, they are named different things in the different files). Call this new dataset mpls_all.
mpls_nbhd <- st_read("Minneapolis_Neighborhoods/Minneapolis_Neighborhoods.shp", quiet = TRUE)
mpls_all<-
mpls_nbhd%>%
right_join(MplsDemo,
by = c("BDNAME" = "neighborhood"))%>%
right_join(mpls_suspicious,
by = c("BDNAME" = "neighborhood"))
leaflet to create a map from the mpls_all data that colors the neighborhoods by prop_suspicious. Display the neighborhood name as you scroll over it. Describe what you observe in the map.colors_mpls<-colorNumeric("YlOrBr",
domain = mpls_all$prop_sus)
leaflet(mpls_all)%>%
addTiles()%>%
addPolygons(fillColor = ~colors_mpls(prop_sus),
fillOpacity = .7,
label = ~paste(str_to_title(BDNAME),
":",
round(prop_sus,2),
.sep=""),
highlight = highlightOptions(color="black",
fillOpacity = 1,
bringToFront = FALSE))
leaflet to create a map of your own choosing. Come up with a question you want to try to answer and use the map to help answer that question. Describe what your map shows.starbucks_mn<-
Starbucks%>%
filter(`State/Province` == "MN")
leaflet(data = starbucks_mn) %>%
addTiles()%>%
addCircles(lng = ~Longitude,
lat = ~Latitude,
label = ~`Store Name`,
weight = 3,
opacity = 1)
DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?